Novel Approach For Prediction Research Articles

Hospital Re-Admission Prediction Using Named Entity Recognition and Explainable Machine Learning.

Early hospital readmission refers to unplanned emergency admission of patients within 30 days of discharge. Predicting early readmission risk before discharge can help to reduce the cost of readmissions for hospitals and decrease the death rate for Intensive Care Unit patients. In this paper, we propose a novel approach for prediction of unplanned hospital readmissions using discharge notes from the MIMIC-III database. This approach is based on first extracting relevant information from clinical reports using a pretrained Named Entity Recognition model called BioMedical-NER, which is built on Bidirectional Encoder Representations from Transformers architecture, with the extracted features then used to train machine learning models to predict unplanned readmissions. Our proposed approach achieves better results on clinical reports compared to the state-of-the-art methods, with an average precision of 88.4% achieved by the Gradient Boosting algorithm. In addition, explainable Artificial Intelligence techniques are applied to provide deeper comprehension of the predictive results.

A Novel Approach to Heart Disease Prediction Using Artificial Intelligence Techniques

INTRODUCTION: Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient prediction models OBJECTIVES: To research new models for heart disease prediction METHODS: This paper presents a novel approach for predicting heart disease using advanced artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms RESULTS By leveraging patient data and integrating various AI models, this approach aims to enhance prediction accuracy and support early diagnosis and intervention CONCLUSION: This study presents a novel AI-based approach for heart disease prediction, demonstrating the efficacy of ML and DL models in improving diagnostic accuracy

A novel approach for prediction of groundwater quality using gradient boosting-based algorithms

ABSTRACT This study explores the potential of the GPBoost approach for groundwater quality assessment in comparison to three other gradient boosting-based algorithms. Three methods, random search, grid search, and Bayesian optimization were used to find the optimal values of various hyperparameters with all four-gradient boosting-based algorithms. One hundred and two samples of Entropy weighted water quality index with 14 input parameters are used for assessing groundwater quality. The calculated EWQI values for drinking range between 80.4 and 394.96 in pre-monsoon and 39.6 to 338.79 during the post-monsoon period. Moreover, spatial distribution maps displayed that the central portions of the study area fall under medium water quality. The performances of models were compared based on multiple statistical criteria, including Correlation Coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). The results reveal that the CC value by all modeling approaches is more than 0.93, suggesting a comparable performance by all methods. Results in terms of RMSE values in predicting the EWQI values suggest GPBoost (random search) model performed better than the other three models, thus suggesting a competitive performance by GPBoost in comparison to other gradient boosting-based approaches. Relative importance analysis provided by random and grid search methods highlights the significance of NO3 −, Mg2+, TDS, EC, and TH as important input parameters for predicting EWQI.

A Novel Approach for Prediction of Lung Disease Using Chest X-ray Images Based on DenseNet and MobileNet.

Covid19 corona virus has caused widespread disruption across the world, in terms of the health, economy, and society problems. X-ray images of the chest can be helpful in making an accurate diagnosis because the corona virus typically first manifests its symptoms in patients' lungs. In this study, a classification method based on deep learning is proposed as a means of identifying lung disease from chest X-ray images. In the proposed study, the detection of covid19 corona virus disease from chest X-ray images was made with MobileNet and Densenet models, which are deep learning methods. Several different use cases can be built with the help of MobileNet model and case modelling approach is utilized to achieve 96% accuracy and an Area Under Curve (AUC) value of 94%. According to the result, the proposed method may be able to more accurately identify the signs of an impurity from dataset of chest X-ray images. This research also compares various performance parameters such as precision, recall and F1-Score.

GLSTM: A novel approach for prediction of real & synthetic PID diabetes data using GANs and LSTM classification model

Generative Adversarial Network (GAN) is a revolution in modern artificial systems. Deep learning-based Generative adversarial networks generate realistic synthetic tabular data. Synthetic data are used to enhance the size of a relatively small training dataset while ensuring the confidentiality of the original data. In this context, we implemented the GAN framework for generating diabetes data to help the health care professional in more clinical applications. GAN is used to validate the Pima Indian Diabetes (PID) Dataset. Various preprocessing techniques, such as handling missing values, outliers and data imbalance problems, enhance data quality. Some exploratory data analyses, such as heat maps, bar graphs and histograms, are used for data visualisation. We employed hypothesis testing to examine the resemblance between real data and GAN-generated synthetic data. In this study, we proposed a GAN-Long Short-Term Memory (GLSTM) system, in which GAN is used for data augmentation, and LSTM is used for diabetes classification. Additionally, various GAN models such as CTGAN, Vanilla GAN, Coupula GAN, Gaussian Coupula GAN, and TVAE GAN are used to generate the synthetic dataset. Experiments were conducted on real data, synthetic data, and by combining real and synthetic data. The model that used both real and synthetic data obtained a substantially better accuracy of 97% compared to 92% when only real data was used. We also observed that synthetic data could be used in place of real data, as the mean correlation between synthetic and real data is 0.93. Our study's findings outperformed when compared to state-of-the-art methodologies.

A novel approach for prediction of transverse thermal conductivity of unidirectional random fiber composites

In fiber-reinforced composites, fibers are randomly positioned during the manufacturing process. Most of the theoretical studies assume regular arrangement of fibers in matrix that permits the use of representative volume element (RVE) for prediction of mechanical properties. Theoretical methods established in the literature for random fiber composites are complex and approximate. A simple approach is established in this work to accurately predict the transverse thermal conductivity of unidirectional random fiber-reinforced composites using the principle of electrical analogy (EA). In this study, circular fiber is transformed into an equivalent square fiber that facilitates the application of EA, and predicted the transverse thermal conductivity of random fiber composites. The conductivity of equivalent square fiber is evaluated by back-track approach using an equivalent thermal system. Python code is developed to generate the random location of fibers in the composite. The results obtained in this way are verified and found to be in good agreement with benchmark results over a wide range of fiber composites with varying conductivity ratios, fiber volume fraction, and randomness.

A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors

Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively.

An efficient and sustainable novel approach for prediction of start-up company success rates through sustainable machine learning paradigms

The primary objective is to construct a sustainable machine-learning model that utilizes multiple variables to forecast the success of a startup enterprise. It incorporates a Flask application for creating a user-friendly interface, where users can input specific parameters related to a startup, such as financial metrics, industry sector, and location. These inputs are then passed through a sustainable machine learning prediction model, which has been trained on a comprehensive dataset of startup information. The model employs sustainable advanced algorithms to evaluate their startup ventures' potential success. Through the development and deployment of the Flask application and the integration of sustainable machine learning prediction model, this model contributes to the field of startup analysis and decision-making. It offers a sustainable and efficient solution for predicting startup success, empowering users to make data-backed decisions and optimize their resource allocation.

A novel approach for prediction of exothermic decomposition temperature of energetic complexes through additive and non-additive descriptors

A novel approach for prediction of exothermic decomposition temperature of energetic complexes through additive and non-additive descriptors

A novel approach for prediction of surface roughness in turning of EN353 steel by RVR-PSO using selected features of VMD along with cutting parameters

A novel approach for prediction of surface roughness in turning of EN353 steel by RVR-PSO using selected features of VMD along with cutting parameters

A novel approach for prediction of daily streamflow discharge data using correlation based feature selection and random forest method

The accurate methods for the forecasting of hydrological characteristics are significantly important for water resource management and environmental aspects. In this study, a novel approach for daily streamflow discharge data forecasting is proposed. Streamflow discharge, temperature, and precipitation data were used for feature extraction which were systematically employed for forecasting studies. While the correlation-based feature selection (CFS) was used for feature selection, Random Forest (RF) model is employed for forecasting of following 7 days. Moreover, an accuracy comparison between the RF model and CFS-RF model is drawn by using streamflow discharge data. Acquired results confirmed the accuracy of CFS-RF model for both, middle and extended forecasting times compared to RF model which had similar accuracy values for the closer forecasting times. Moreover, the CFS-RF model proved to be much robust for extended forecasting durations.

A Novel Approach for Prediction of Human Disease using Symptoms by Multilayer Perceptron Algorithm to Improve Accuracy and Compared with K Nearest Neighbor Algorithm

A Novel Approach for Prediction of Human Disease using Symptoms by Multilayer Perceptron Algorithm to Improve Accuracy and Compared with K Nearest Neighbor Algorithm

A Novel Approach for Prediction of Human Disease using Symptoms by Multilayer Perceptron Algorithm to Improve the Accuracy and Compared with Gaussian Naïve Bayes Algorithm

A Novel Approach for Prediction of Human Disease using Symptoms by Multilayer Perceptron Algorithm to Improve the Accuracy and Compared with Gaussian Naïve Bayes Algorithm

A novel mathematical modeling in shift in emotion for gauging the social influential in big data streams with hybrid sarcasm detection

SummaryOnline social network is a platform that plays an essential role in identifying the emotional values of user‐generated content such as blogs, posts, and comments along with their influential factors. Especially on Twitter, network users are growing worldwide day by day and creating a massive amount of data that is not analyzed effectively in a quick way. Identifying the most influential persons on the social network is also a challenging task over the wide range of real‐time applications like recommendation systems. Now, to handle these situations, this article proposes a novel approach for prediction of information diffusion that includes emotion recognition with sarcasm detection based influence spreader identification (PID‐ERSDISI). The proposed method uses the user‐generated posts for emotion recognition in tandem with sarcasm detection both implicitly and explicitly. This approach helps to gauge the leverage that influences spreaders and also enhances the prediction accuracy of information diffusion in a better way. The implementation of the proposed work executed their task one after another in the following way, namely, sarcasm detection, emotional‐level computation, breakpoint computation, breakpoint validation, influential user generation, and information diffusion. After the successful implementation of this proposed PID‐ERSDIS, it produced prominent results against other state‐of‐art methods.

A novel approach for prediction of mass yield and higher calorific value of hydrothermal carbonization by a robust multilinear model and regression trees

This study shows a mathematical and statistical analysis to generate models based on multiple linear regression (MLR) and regression trees (RT) that allow a reliable prediction of the Mass Yield (MY) and the Higher Heating Value (HHV) of the final solid product obtained by Hydrothermal Carbonization, called hydrochar. MLR models were obtained for lignocellulosic and non-lignocellulosic biomass using a set of experimental data with more than 500 points collected from the literature. A new approach based on dimensionless groups of variables that describe the composition of biomass and operational conditions was used. The analysis for each equation indicated that the MY depends on the process conditions and the biomass composition, which is proportional to the Polarity Index (IP) and Reactive Index (IR) values. On the other hand, the severity factor (log Ro) and the initial calorific value (HHVo) were the main factors for the HHV, but also the raw biomass composition (IP and H/C ratio) had an opposite and equal significant effect. For these equations, the results indicated an adjusted R2 (R2a) of about 0.90 and an average RMSE of 6% and 1.7 MJ/kg for MY and HHV, respectively.Besides, explanatory variables were analyzed by their Relative Importance for the RT models. The severity factor (65%) and the IR (18%) were the most decisive variable in the MY prediction. The R2 and RMSE were 0.73 and 2%, respectively. For HHV, the variables with the most significant impact were the HHVo (33%), the log Ro (24%), and the IP (22%). In this case, the R2 and RMSE were 0.87 and 0.68 MJ/kg, respectively. Therefore, the model equations obtained are a powerful tool to predict the mass yield and the energetic value of the hydrochar before developing an experimental study.

A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

In recent decades, due to groundwater withdrawal in the Kabodarahang region, Iran, Hamadan, hazardous events such as sinkholes, droughts, water scarcity, etc., have occurred. This study models groundwater level (GWL) of the Kabodarahang region using two novel techniques including Self-Adaptive Extreme Learning Machine (SAELM) and Wavelet-Self-Adaptive Extreme Learning Machine (WA-SAELM). Using the stepwise selection as different lags along with different input combinations, ten different SAELM and WA-SAELM models were developed. First, the best activation function is chosen for numerical models. After that, GWL values were normalized to equalize the values and enhance speed and accuracy of modeling. Then, an optimized mother wavelet is selected in order to simulate GWLs. Next, the best model was introduced as the superior model in which values of the correlation coefficient (R), Root Mean Squared Error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were obtained 0.969, 0.358 and 0.939, respectively. In addition, the results of the superior model are compared with classical neural network models such as Artificial Neural Network (ANN), Wavelet-Artificial Neural Network (WA-ANN), Support Vector Machine (SVM) and Wavelet-Support Vector Machine (WA-SVM). Among all models, WA-SAELM approximated GWLs with higher accuracy. Furthermore, based on the results obtained from an uncertainty analysis, the superior model was identified as a model with an underestimated performance. Additionally, an explicit and practical matrix was proposed for computing GWLs. Finally, the matrix was validated for another piezometer.

A Novel Approach for Prediction of Sensitivity toward the Electrical Discharge of Quaternary Ammonium‐based Energetic Ionic Liquids or Salts

Sensitivity toward electrical discharge (electric spark or electrostatic sensitivity) is an important safety parameter for assessment of working with new energetic compounds including ionic molecular energetic materials. This work introduces a novel simple correlation to assess the electrostatic sensitivity of quaternary ammonium‐based energetic ionic liquids or salts, which are based on elemental composition and the structure parameters of specific cations and anions. For 66 ionic molecular systems as a training set, the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Maximum of Errors (Max Error) of predictions for the new model are 0.17, 0.15, and 0.31 J, respectively. The reliability of models was also tested for further 21 ionic compounds, which give the values of RMSE, MAE, and Max Error of 0.13, 0.11, and 0.25 J, respectively. Thus, the accuracy of the novel method for prediction of the electrostatic sensitivity of quaternary ammonium‐based energetic ionic liquids or salts is relatively good with respect to high uncertainties in experimental data.

A Novel Approach for Prediction of Heart Disease: Machine Learning Techniques

Heart disease and machine learning are the two different words where one is related to medical field and another one to artificial intelligence. In medical filed most of them are facing the problems with the heart disease and machine learning is developing area in computer science. Heart disease is general called cardiac disease where it gives the more data or information, it is to be collected to give the reports for the patients and the machine learning also requires the data for predicting and to solve the problems. Machine learning techniques are used in prediction of heart diseases where it gives the faster prediction with less computation time and better accuracy to progress their health. Heart disease prediction requires lot of data for predicting and in cloud computing also we have more data and the data available in cloud it is difficult to analyze. So we use machine learning algorithms or techniques to predict the heart disease and the in the similar way we can apply these algorithms or techniques to predict or analyze the data that is available in cloud. In this paper we are going to use machine learning algorithms called Backpropagation Algorithm and later we use optimization algorithm later. Backpropagation algorithm deals with the artificial neural networks. Backpropagation is a method used to calculate the error contribution of each neuron after a batch of data (in image recognition, multiple images) is processed. This is used by an enveloping optimization algorithm to adjust the weight of each neuron, completing the learning process for that case. Machine learning algorithms and techniques are used for recognize the intensity of risk issues in humans and it helps the patients to take safety measures in well advances to save the patient’s life.

Application of Grid partitioning based Fuzzy inference system method as a novel approach for prediction of interfacial tension of hydrocarbon and carbon dioxide

ABSTRACTNowadays the importance of enhanced oil recovery (EOR) processes increases because of increasing demand of energy and declination of oil reservoirs. Due to this fact the researchers attracted to study performance of EOR methods. one of the high efficient methods is carbon dioxide injection which is favorable because of low cost and environmental friendly viewpoints. One of important parameters which have straight effect on recovery of injection is interfacial tension between carbon dioxide and hydrocarbons. In the present investigation the main objective is proposing the Grid partitioning based Fuzzy inference system method as novel approach to predict interfacial tension of carbon dioxide and hydrocarbon in terms of temperature, pressure, liquid and gas densities and molecular weight of alkane. The coefficients of determination for different datasets of training and testing of estimating algorithm are determined as 0.9919 and 0.9899. This results express the algorithm has potential of estimating interfacial tension of hydrocarbons and carbon dioxide.

Novel Approach for Prediction of Localized Necking in Case of Nonlinear Strain Paths

Rising customer expectations regarding design complexity and weight reduction of sheet metal components alongside with further reduced time to market implicate increased demand for process validation using numerical forming simulation. Formability prediction though often is still based on the forming limit diagram first presented in the 1960s. Despite many drawbacks in case of nonlinear strain paths and major advances in research in the recent years, the forming limit curve (FLC) is still one of the most commonly used criteria for assessing formability of sheet metal materials. Especially when forming complex part geometries nonlinear strain paths may occur, which cannot be predicted using the conventional FLC-Concept. In this paper a novel approach for calculation of FLCs for nonlinear strain paths is presented. Combining an interesting approach for prediction of FLC using tensile test data and IFU-FLC-Criterion a model for prediction of localized necking for nonlinear strain paths can be derived. Presented model is purely based on experimental tensile test data making it easy to calibrate for any given material. Resulting prediction of localized necking is validated using an experimental deep drawing specimen made of AA6014 material having a sheet thickness of 1.04 mm. The results are compared to IFU-FLC-Criterion based on data of pre-stretched Nakajima specimen.